SingleCellDesk Example Analysis

📘 Experimental Design Summary

This analysis is based on the single-cell RNA-seq dataset published in:

Tu, J. et al. (2021). Single-Cell Transcriptomics of Human Nucleus Pulposus Cells: Understanding Cell Heterogeneity and Degeneration. Advanced Science, 8(23), 2103631. https://doi.org/10.1002/advs.202103631

Study Objectives:

• Profile transcriptional heterogeneity in human nucleus pulposus cells (NPCs).
• Compare non-degenerated (Grade II) and degenerated (Grade III–IV) intervertebral discs.
• Identify distinct NPC subtypes and reconstruct cellular trajectories.

Data Source:

• GEO accession: GSE165722
• Technology: BD Rhapsody platform
• Samples: 8 NPC tissue samples with varying degeneration states.

Summary of Analysis Flow:

1. QC and Filtering: Remove low-quality cells with high mitochondrial content.
2. Normalization and Clustering: Use Seurat to identify transcriptionally distinct clusters.
3. Marker Gene Detection: Find genes distinguishing each cluster.
4. Cluster Annotation: Label clusters into known biological subtypes.
5. Pseudotime Analysis: Use Monocle3 to infer differentiation trajectories.

Interpretation:

From the plots generated: - UMAP/tSNE: Reveal 6 biologically interpretable NPC subtypes. - DotPlot: Confirms canonical marker expression in annotated subtypes. - Pseudotime: Suggests HT-CLNPs are early progenitors transitioning into mature states such as effector or fibroNPCs.

📥 Choose Dataset Format

This analysis use dataset “GSE165722”.

🔬 Step 1: Quality Control

QC Violin Plot

QC Violin Plot

🔬 Step 2: Normalization and Clustering

UMAP Clustering (Default)

UMAP Clusters

t-SNE Clustering with Cell Types

tSNE Clusters by Cell Type

🔬 Step 3: Marker Identification and Annotation

Marker Genes (Preview)

##   p_val avg_log2FC pct.1 pct.2 p_val_adj cluster  gene
## 1     0  1.8229819 0.868 0.317         0       0 CYR61
## 2     0  1.2440728 0.977 0.431         0       0   DCN
## 3     0  1.8586392 0.877 0.345         0       0  CTGF
## 4     0  1.3793767 0.947 0.417         0       0   LUM
## 5     0  1.7458917 0.940 0.456         0       0   FN1
## 6     0  0.6984552 0.915 0.438         0       0   CLU

DotPlot of Top Markers

Top Marker DotPlot

Barplot: Cell Type Distribution

Cell Type Distribution Barplot

🔬 Step 4: Pseudotime Inference with Monocle3

Pseudottime Trajectory

Pseudotime trajectory of NPC populations

🔬 Step 5: Cell-Cell Communication with CellChat

📌 Notes

• For GSE165722, the .counts.tsv.gz and .cellname.txt.gz pairs are expected in data/GSE165722/extracted/
• For 10x, use individual filtered_feature_bc_matrix/ directories for each sample. ## 📊 Additional Static Results